SCZ - Brain Anterior cingulate cortex BA24
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20211220) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
| absolute | relative |
|---|---|
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ | data |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R | code/ctwas_config.R |
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version d57314b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .ipynb_checkpoints/
Ignored: data/AF/
Untracked files:
Untracked: Rplot.png
Untracked: analysis/.ipynb_checkpoints/
Untracked: analysis/SCZ_2020_Brain_Amygdala.Rmd
Untracked: analysis/SCZ_2020_Brain_Anterior_cingulate_cortex_BA24.Rmd
Untracked: analysis/SCZ_2020_Brain_Caudate_basal_ganglia.Rmd
Untracked: analysis/SCZ_2020_Brain_Cerebellar_Hemisphere.Rmd
Untracked: analysis/SCZ_2020_Brain_Cerebellum.Rmd
Untracked: analysis/SCZ_2020_Brain_Hippocampus.Rmd
Untracked: analysis/SCZ_2020_Brain_Nucleus_accumbens_basal_ganglia.Rmd
Untracked: analysis/SCZ_2020_Brain_Spinal_cord_cervical_c-1.Rmd
Untracked: analysis/SCZ_2020_Brain_Substantia_nigra.Rmd
Untracked: code/.ipynb_checkpoints/
Untracked: code/AF_out/
Untracked: code/Autism_out/
Untracked: code/BMI_S_out/
Untracked: code/BMI_out/
Untracked: code/Glucose_out/
Untracked: code/LDL_S_out/
Untracked: code/SCZ_2014_EUR_out/
Untracked: code/SCZ_2020_out/
Untracked: code/SCZ_S_out/
Untracked: code/SCZ_out/
Untracked: code/T2D_out/
Untracked: code/ctwas_config.R
Untracked: code/mapping.R
Untracked: code/out/
Untracked: code/run_AF_analysis.sbatch
Untracked: code/run_AF_analysis.sh
Untracked: code/run_AF_ctwas_rss_LDR.R
Untracked: code/run_Autism_analysis.sbatch
Untracked: code/run_Autism_analysis.sh
Untracked: code/run_Autism_ctwas_rss_LDR.R
Untracked: code/run_BMI_analysis.sbatch
Untracked: code/run_BMI_analysis.sh
Untracked: code/run_BMI_analysis_S.sbatch
Untracked: code/run_BMI_analysis_S.sh
Untracked: code/run_BMI_ctwas_rss_LDR.R
Untracked: code/run_BMI_ctwas_rss_LDR_S.R
Untracked: code/run_Glucose_analysis.sbatch
Untracked: code/run_Glucose_analysis.sh
Untracked: code/run_Glucose_ctwas_rss_LDR.R
Untracked: code/run_LDL_analysis_S.sbatch
Untracked: code/run_LDL_analysis_S.sh
Untracked: code/run_LDL_ctwas_rss_LDR_S.R
Untracked: code/run_SCZ_2014_EUR_analysis.sbatch
Untracked: code/run_SCZ_2014_EUR_analysis.sh
Untracked: code/run_SCZ_2014_EUR_ctwas_rss_LDR.R
Untracked: code/run_SCZ_2020_analysis.sbatch
Untracked: code/run_SCZ_2020_analysis.sh
Untracked: code/run_SCZ_2020_ctwas_rss_LDR.R
Untracked: code/run_SCZ_analysis.sbatch
Untracked: code/run_SCZ_analysis.sh
Untracked: code/run_SCZ_analysis_S.sbatch
Untracked: code/run_SCZ_analysis_S.sh
Untracked: code/run_SCZ_ctwas_rss_LDR.R
Untracked: code/run_SCZ_ctwas_rss_LDR_S.R
Untracked: code/run_T2D_analysis.sbatch
Untracked: code/run_T2D_analysis.sh
Untracked: code/run_T2D_ctwas_rss_LDR.R
Untracked: code/wflow_build.R
Untracked: code/wflow_build.sbatch
Untracked: data/.ipynb_checkpoints/
Untracked: data/BMI/
Untracked: data/PGC3_SCZ_wave3_public.v2.tsv
Untracked: data/SCZ/
Untracked: data/SCZ_2014_EUR/
Untracked: data/SCZ_2020/
Untracked: data/SCZ_S/
Untracked: data/T2D/
Untracked: data/UKBB/
Untracked: data/UKBB_SNPs_Info.text
Untracked: data/gene_OMIM.txt
Untracked: data/gene_pip_0.8.txt
Untracked: data/mashr_Heart_Atrial_Appendage.db
Untracked: data/mashr_sqtl/
Untracked: data/summary_known_genes_annotations.xlsx
Untracked: data/untitled.txt
Unstaged changes:
Modified: analysis/SCZ_2020_Brain_Cortex.Rmd
Modified: analysis/SCZ_2020_Brain_Frontal_Cortex_BA9.Rmd
Modified: analysis/SCZ_2020_Brain_Hypothalamus.Rmd
Modified: analysis/SCZ_2020_Brain_Putamen_basal_ganglia.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10920#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1075 755 637 422 525 619 523 417 398 426 655 624 222 367 373 505
17 18 19 20 21 22
637 172 840 330 119 279 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8752#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8015Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0160390 0.0002704 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.63 12.35 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10920 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01696 0.15297 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05971 0.79059Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10719 ZNF823 19_10 0.9944 42.57 0.0002623 6.363 2
11817 AC012074.2 2_15 0.9849 30.78 0.0001878 5.469 1
9404 LSMEM2 3_35 0.9812 976.41 0.0059357 4.271 1
2571 TRPV4 12_66 0.9171 24.32 0.0001382 4.416 1
881 KLHL20 1_85 0.9018 34.15 0.0001908 5.800 1
11000 HLA-DMA 6_27 0.8911 80.60 0.0004450 -9.622 2
5988 FAM135B 8_91 0.8845 22.44 0.0001230 -3.461 1
5226 C12orf10 12_33 0.8768 24.05 0.0001306 -4.963 1
392 RETSAT 2_54 0.8725 20.77 0.0001123 3.963 1
4596 DAGLA 11_34 0.8724 21.84 0.0001180 -4.263 1
11773 HIST1H2BN 6_21 0.8688 182.41 0.0009818 13.182 1
6874 ACE 17_37 0.8598 34.26 0.0001825 -5.802 1
108 ELAC2 17_11 0.8590 23.58 0.0001255 5.380 1
63 KMT2E 7_65 0.8574 54.92 0.0002918 -7.571 2
3251 ABCG2 4_59 0.8523 20.31 0.0001072 -3.954 1
7388 GNL3 3_37 0.8488 67.71 0.0003561 9.429 1
5336 CPNE2 16_30 0.8420 21.10 0.0001101 -4.125 1
10370 KCTD21 11_43 0.8406 21.22 0.0001105 4.176 3
11153 KRT81 12_32 0.8276 20.22 0.0001037 4.046 3
11577 LINC01268 6_75 0.8262 21.73 0.0001112 -4.406 1
786 ACADVL 17_6 0.8249 22.67 0.0001158 -4.405 1
146 CELSR3 3_34 0.8167 24.91 0.0001261 4.957 2
2838 PDCD10 3_103 0.8139 23.18 0.0001169 -4.520 1
11413 FANCG 9_27 0.8083 23.32 0.0001168 -4.512 1
11485 LINC00320 21_6 0.8014 31.17 0.0001548 5.610 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9404 LSMEM2 3_35 9.812e-01 976.41 5.936e-03 4.2709 1
212 SEMA3B 3_35 1.430e-06 952.77 8.440e-09 1.0870 1
10292 SLC38A3 3_35 3.286e-07 241.37 4.914e-10 -2.7756 1
127 CACNA2D2 3_35 1.466e-06 217.88 1.980e-09 -0.1392 1
38 RBM6 3_35 5.848e-01 189.79 6.876e-04 4.4688 1
2874 HEMK1 3_35 3.890e-06 183.71 4.427e-09 0.4441 1
11773 HIST1H2BN 6_21 8.688e-01 182.41 9.818e-04 13.1822 1
10138 HYAL3 3_35 1.115e-07 165.11 1.141e-10 -2.5066 1
7423 CAMKV 3_35 1.227e-05 165.05 1.255e-08 -1.7107 1
9 SEMA3F 3_35 7.549e-03 156.39 7.314e-06 4.0127 1
7425 MST1R 3_35 1.636e-04 143.37 1.453e-07 -4.0250 1
13060 RP1-86C11.7 6_21 1.862e-01 124.68 1.438e-04 10.5382 1
13189 LINC02019 3_35 2.585e-06 109.28 1.751e-09 0.3233 2
2875 CISH 3_35 9.531e-07 105.36 6.222e-10 -0.1383 1
7420 RNF123 3_35 1.051e-07 93.36 6.077e-11 -2.3622 1
11035 ABHD16A 6_26 5.043e-01 90.21 2.819e-04 10.7104 1
11039 APOM 6_26 2.953e-01 88.81 1.625e-04 10.6484 1
10109 BTN3A2 6_20 5.022e-02 85.92 2.673e-05 7.6871 3
11710 CYP21A2 6_26 3.482e-02 85.79 1.851e-05 -10.4143 1
12066 C4A 6_26 2.412e-02 83.15 1.242e-05 10.3289 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9404 LSMEM2 3_35 0.9812 976.41 0.0059357 4.271 1
11773 HIST1H2BN 6_21 0.8688 182.41 0.0009818 13.182 1
38 RBM6 3_35 0.5848 189.79 0.0006876 4.469 1
11000 HLA-DMA 6_27 0.8911 80.60 0.0004450 -9.622 2
7388 GNL3 3_37 0.8488 67.71 0.0003561 9.429 1
63 KMT2E 7_65 0.8574 54.92 0.0002918 -7.571 2
11035 ABHD16A 6_26 0.5043 90.21 0.0002819 10.710 1
10719 ZNF823 19_10 0.9944 42.57 0.0002623 6.363 2
2969 SF3B1 2_117 0.7574 52.96 0.0002485 7.605 1
7973 GATAD2A 19_15 0.7026 52.29 0.0002276 -7.406 2
881 KLHL20 1_85 0.9018 34.15 0.0001908 5.800 1
11817 AC012074.2 2_15 0.9849 30.78 0.0001878 5.469 1
6874 ACE 17_37 0.8598 34.26 0.0001825 -5.802 1
8291 INO80E 16_24 0.4994 53.26 0.0001648 7.551 1
11039 APOM 6_26 0.2953 88.81 0.0001625 10.648 1
7911 PDIA3 15_16 0.6730 38.00 0.0001585 6.314 1
11485 LINC00320 21_6 0.8014 31.17 0.0001548 5.610 1
9437 PUF60 8_94 0.7171 34.53 0.0001534 5.793 1
426 FAM120A 9_47 0.7732 31.55 0.0001511 -5.577 2
13060 RP1-86C11.7 6_21 0.1862 124.68 0.0001438 10.538 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11773 HIST1H2BN 6_21 0.8687556 182.41 9.818e-04 13.182 1
11035 ABHD16A 6_26 0.5043393 90.21 2.819e-04 10.710 1
11039 APOM 6_26 0.2952848 88.81 1.625e-04 10.648 1
13060 RP1-86C11.7 6_21 0.1862044 124.68 1.438e-04 10.538 1
11710 CYP21A2 6_26 0.0348181 85.79 1.851e-05 -10.414 1
12066 C4A 6_26 0.0241153 83.15 1.242e-05 10.329 2
6075 CNNM2 10_66 0.1968619 49.35 6.019e-05 -9.686 1
11000 HLA-DMA 6_27 0.8910906 80.60 4.450e-04 -9.622 2
455 MPHOSPH9 12_75 0.1238971 74.99 5.756e-05 9.460 1
7388 GNL3 3_37 0.8487735 67.71 3.561e-04 9.429 1
11034 LY6G6C 6_26 0.0067577 50.48 2.114e-06 8.911 2
7389 PBRM1 3_37 0.0074522 55.96 2.584e-06 -8.722 1
6209 ABCB9 12_75 0.0008853 65.35 3.584e-07 8.638 1
9616 ARL6IP4 12_75 0.0008112 64.92 3.263e-07 8.615 1
8109 GLYCTK 3_37 0.0622660 75.32 2.906e-05 8.577 1
12110 TWF2 3_37 0.0393235 68.94 1.680e-05 -8.347 1
9474 HARBI1 11_28 0.3543604 59.61 1.309e-04 8.046 1
8984 ATG13 11_28 0.3543604 59.61 1.309e-04 -8.046 1
2524 MDK 11_28 0.1255455 57.18 4.447e-05 -7.898 1
2895 NEK4 3_37 0.0057114 48.81 1.727e-06 7.898 1Comparing z scores and PIPs
[1] 0.01566GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 79Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
30 Confusion 0.03072 1/32 1/9703
55 Gingival Hypertrophy 0.03072 1/32 1/9703
68 Infant, Premature, Diseases 0.03072 1/32 1/9703
111 Pneumonia, Viral 0.03072 1/32 1/9703
143 Left Ventricular Hypertrophy 0.03072 2/32 25/9703
164 Speech impairment 0.03072 1/32 1/9703
165 Derealization 0.03072 1/32 1/9703
175 Spondylometaphyseal dysplasia, Kozlowski type 0.03072 1/32 1/9703
176 Metatropic dwarfism 0.03072 1/32 1/9703
203 Brachyolmia Type 3 0.03072 1/32 1/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLPIP Manhattan Plot
Warning: 'timedatectl' indicates the non-existent timezone name 'n/a'Warning: Your system is mis-configured: '/etc/localtime' is not a symlinkWarning: It is strongly recommended to set envionment variable TZ to 'America/
Chicago' (or equivalent)Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 130#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 59#significance threshold for TWAS
print(sig_thresh)[1] 4.583#number of ctwas genes
length(ctwas_genes)[1] 25#number of TWAS genes
length(twas_genes)[1] 171#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
392 RETSAT 2_54 0.8725 20.77 0.0001123 3.963 1
9404 LSMEM2 3_35 0.9812 976.41 0.0059357 4.271 1
2838 PDCD10 3_103 0.8139 23.18 0.0001169 -4.520 1
3251 ABCG2 4_59 0.8523 20.31 0.0001072 -3.954 1
11577 LINC01268 6_75 0.8262 21.73 0.0001112 -4.406 1
5988 FAM135B 8_91 0.8845 22.44 0.0001230 -3.461 1
11413 FANCG 9_27 0.8083 23.32 0.0001168 -4.512 1
4596 DAGLA 11_34 0.8724 21.84 0.0001180 -4.263 1
10370 KCTD21 11_43 0.8406 21.22 0.0001105 4.176 3
11153 KRT81 12_32 0.8276 20.22 0.0001037 4.046 3
2571 TRPV4 12_66 0.9171 24.32 0.0001382 4.416 1
5336 CPNE2 16_30 0.8420 21.10 0.0001101 -4.125 1
786 ACADVL 17_6 0.8249 22.67 0.0001158 -4.405 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03077 0.18462 #specificity
print(specificity) ctwas TWAS
0.9981 0.9865 #precision / PPV
print(precision) ctwas TWAS
0.1600 0.1404 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 59#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 736#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.583#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 6#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 71#sensitivity / recall
sensitivity ctwas TWAS
0.0678 0.4068 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9973 0.9361 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6667 0.3380 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)
abline(a=0,b=1,lty=3)
#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants
#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))silver_standard_case
Not Imputed Insignificant z-score Nearby SNP(s)
71 35 19
Detected (PIP > 0.8) Nearby Bystander Gene
4 1 #show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]named character(0)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.1
[4] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[7] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
[10] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tidyverse_1.3.1 tibble_3.1.6
[16] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0
[19] cowplot_1.1.1 ggplot2_3.3.5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2 rjson_0.2.20
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13 farver_2.1.0
[10] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.46.0
[13] fansi_1.0.2 lubridate_1.8.0 xml2_1.3.3
[16] codetools_0.2-16 doParallel_1.0.17 cachem_1.0.6
[19] knitr_1.36 jsonlite_1.7.2 apcluster_1.4.8
[22] Cairo_1.5-12.2 broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.2-18 fastmap_1.1.0
[31] cli_3.1.0 later_0.8.0 prettyunits_1.1.1
[34] htmltools_0.5.2 tools_3.6.1 igraph_1.2.10
[37] GenomeInfoDbData_1.2.1 gtable_0.3.0 glue_1.6.2
[40] reshape2_1.4.4 doRNG_1.8.2 Rcpp_1.0.8
[43] Biobase_2.44.0 cellranger_1.1.0 jquerylib_0.1.4
[46] vctrs_0.3.8 svglite_1.2.2 iterators_1.0.14
[49] xfun_0.29 ps_1.6.0 rvest_1.0.2
[52] lifecycle_1.0.1 rngtools_1.5.2 XML_3.99-0.3
[55] zlibbioc_1.30.0 getPass_0.2-2 scales_1.1.1
[58] vroom_1.5.7 hms_1.1.1 promises_1.0.1
[61] yaml_2.2.1 curl_4.3.2 memoise_2.0.1
[64] ggrastr_1.0.1 gdtools_0.1.9 stringi_1.7.6
[67] RSQLite_2.2.8 highr_0.9 foreach_1.5.2
[70] rlang_1.0.1 pkgconfig_2.0.3 bitops_1.0-7
[73] evaluate_0.14 lattice_0.20-38 labeling_0.4.2
[76] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[79] plyr_1.8.6 magrittr_2.0.2 R6_2.5.1
[82] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[85] haven_2.4.3 whisker_0.3-2 withr_2.4.3
[88] RCurl_1.98-1.5 modelr_0.1.8 crayon_1.5.0
[91] utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[94] progress_1.2.2 grid_3.6.1 data.table_1.14.2
[97] blob_1.2.2 callr_3.7.0 git2r_0.26.1
[100] reprex_2.0.1 digest_0.6.29 httpuv_1.5.1
[103] munsell_0.5.0 beeswarm_0.2.3 vipor_0.4.5